Even more impressive, though, is that the AI can solve a classic machine learning problem: correctly identifying handwritten numbers.

The work—a “significant step” toward demonstrating the capacity to program artificial intelligence into synthetic biomolecular circuits—was published in the journal Nature.

“Though scientists have only just begun to explore creating artificial intelligence in molecular machines, its potential is already undeniable,” Lulu Qian, assistant professor of bioengineering at Caltech, said in a statement.

“Similar to how electronic computers and smartphones have made humans more capable than a hundred years ago,” she explained, “artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come.”

Ultimately, Qian’s team hopes to program intelligent behavior—the ability to compute, make choices, etc.—in artificial neural networks made of DNA.

Similar to the human brain on which they are founded, these machines function like a system of neurons, and are capable of processing complex information.

“We have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible,” Qian boasted, comparing her technology to a roundworm, which makes simple decisions using just a few hundred neurons.

Far from the uniform digital fonts we’re used to, human handwriting is profoundly varied. So when you try to read the sloppy sequence of numbers that cute guy from the bar scribbled onto a napkin, your brain must perform complex computational tasks to make out the phone number.

Similarly, neural networks must be “taught” to recognize numbers, account for variations in handwriting, and compare their “memories” to decode the writing.

In this case, Cherry built a neural network of carefully designed DNA sequences that identify “molecular handwriting”—numbers and letters made of unique DNA strands.

Deoxyribonucleic acid is made of four basic nucleotides, which bind together in specific combinations to form the double helix of DNA. These predictable patterns make nucleotide strands perfect for computing devices.

Initial tests have shown great promise; Qian and Cherry plan to further develop their artificial neural network to perform different tasks.

“Common medical diagnostics detect the presence of a few biomolecules, for example, cholesterol or blood glucose,” Cherry said. “Using more sophisticated biomolecular circuits like ours, diagnostic testing could one day include hundreds of biomolecules, with the analysis and response conducted directly in the molecular environment.”